ArticleScientific reports2017
Electronic Health Record Driven Prediction for Gestational Diabetes Mellitus in Early Pregnancy.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Machine learning prediction of STEMI incidence with SHAP interpretation of environmental determinants.Scientific reports · 2025Article
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.Journal of medical Internet research · 2025Article
- Development and validation of a prediction model for gestational diabetes mellitus risk among women from 8 to 14 weeks of gestation in Western China.BMC pregnancy and childbirth · 2025Article
- Predicting Prenatal Depression and Assessing Model Bias Using Machine Learning Models.Biological psychiatry global open science · 2024Article
- Improvement effect of insulin resistance in one-day outpatient service by reducing stress adaptation disorders in patients with gestational diabetes mellitus.Frontiers in nutrition · 2024Article
- Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support.Diagnostics (Basel, Switzerland) · 2023Article
- An Evaluation of the Second Trimester Thyroid Function Test in Gestational Diabetes Mellitus: A Case-Control Study.Cureus · 2023Article
- Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques.Medical & biological engineering & computing · 2023Article
- Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning.Journal of clinical medicine · 2023Article
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
- Computational Phenotyping of OMOP CDM Normalized EHR for Prenatal and Postpartum Episodes: An Informatics Framework and Clinical Implementation on All of Us.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Prevalence of Gestational Diabetes in Iran: A Systematic Review and Meta-analysis.Medical journal of the Islamic Republic of Iran · 2023Review
- Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus.Scientific reports · 2022Article
- Sequential Data-Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development.Journal of medical Internet research · 2022Article
- Algorithmic identification of atypical diabetes in electronic health record (EHR) systems.PloS one · 2022Article
- Stacking Ensemble Method for Gestational Diabetes Mellitus Prediction in Chinese Pregnant Women: A Prospective Cohort Study.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gestational diabetes mellitus (GDM) is conventionally confirmed with oral glucose tolerance test (OGTT) in 24 to 28 weeks of gestation, but it is still uncertain whether it can be predicted with secondary use of electronic health records (EHRs) in early pregnancy. To this purpose, the cost-sensitive hybrid model (CSHM) and five conventional machine learning methods are used to construct the predictive models, capturing the future risks of GDM in the temporally aggregated EHRs. The experimental data sources from a nested case-control study cohort, containing 33,935 gestational women in West China Second Hospital. After data cleaning, 4,378 cases and 50 attributes are stored and collected for the data set. Through selecting the most feasible method, the cost parameter of CSHM is adapted to deal with imbalance of the dataset. In the experiment, 3940 samples are used for training and the rest 438 samples for testing. Although the accuracy of positive samples is barely acceptable (62.16%), the results suggest that the vast majority (98.4%) of those predicted positive instances are real positives. To our knowledge, this is the first study to apply machine learning models with EHRs to predict GDM, which will facilitate personalized medicine in maternal health management in the future.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.